In their own words

Before this training, I had already gone through an AI fellowship, so I thought I understood the concepts fairly well. This programme proved me wrong in the best way. The online self-paced modules gave me a solid, flexible foundation, but it was the 5-day intensive onsite sessions that truly transformed my understanding. Mathematical formulas that had always looked complex and intimidating suddenly came alive. The facilitators broke the formulas down with such clarity and practical application that everything just clicked.

My favourite part was the daily “Explain It Back” sessions, where I had to explain the day's technical topics myself and also listen to others’ explanations of the topic. This brought so much more clarity than just sitting in class and listening to the facilitators. The training was rigorous, hands-on, and deeply impactful. I will now teach and think about AI differently. Thank you so much for this incredible experience. I must say that every hour invested was absolutely worth it.

The workshop provided me with a deeper understanding of various aspects of artificial intelligence (AI), including large language models (LLMs), transformer architectures, graphics processing units (GPUs), and neural networks. It also introduced me to the “Explain It Back” teaching method, which I intend to adopt in my classes to enhance students’ understanding and retention of concepts.

The workshop was truly a “learn, unlearn, and relearn” experience for me. In just one week, I gained valuable insights into LLMs, AI, and the technology behind them, while also developing a deeper appreciation of the possibilities and limitations of AI.

What stood out most was the intentionality with which the programme was delivered. The well-curated learning resources, hands-on exercises, group interactions, and practical sessions made the experience engaging and impactful.

I particularly appreciate the facilitators’ commitment to ensuring that everyone was carried along. Their guidance, openness to questions, and ability to simplify complex concepts made the learning process both inclusive and enjoyable.

Overall, it was an inspiring and enriching experience that not only expanded my knowledge of AI but also sparked a stronger desire to keep learning, exploring, and experimenting with LLMs.

I participated in the Africa AI Upskilling Programme – AI Research Foundations Nigeria Cohort 1 Workshop in August, and it was a transformative learning experience. Coming from a neuroscience and medical education background with limited coding experience, I initially approached the programme with curiosity but some uncertainty about navigating AI concepts. However, the excellent facilitators made complex topics accessible by breaking them down into understandable components.

Learning about language models, neural networks, transformer architectures, and model fine-tuning strengthened my confidence in engaging with AI. The collaborative sessions with course mates from computer science backgrounds, particularly the “Explain It Back” approach, were especially valuable. It reinforced the importance of clear communication and deep understanding, a method I have already begun incorporating into my teaching. The programme has reshaped my view of AI as a powerful tool that can complement neuroscience research, medical education, and scientific discovery.

Participating in the Africa AI Upskilling Programme was a truly rewarding and transformative experience. Coming from a medical background with limited exposure to computer science and AI terminology, I initially found the technical concepts unfamiliar. However, the supportive and collaborative learning environment made even complex concepts approachable and engaging.

I particularly enjoyed the “Explain It Back” sessions, which challenged me to not only understand new concepts but also communicate them clearly. I gained valuable knowledge of tokenisation, fine-tuning, generalisation, bias and variance, key-query-value mechanisms, model validation, underfitting and overfitting, gradients, and other fundamental concepts underlying AI and large language models. The technical lectures, responsible innovation sessions, and “Learn It Yourself” activities were equally insightful and practical.

I am especially grateful to my team members for their patience and encouragement throughout the training. The experience broadened my perspective on how AI can be applied to biomedical research, neuroscience, education, and scientific communication.

More importantly, the programme gave me greater confidence to engage with AI and appreciate the possibilities that emerge when medicine, science, education, and technology intersect. I am grateful to the organisers, facilitators, mentors, and my team for making the experience so enriching and memorable.

As a medical student, the aspect of the in-person seminar that stood out to me the most was the “Lecture It Back” sessions. They helped me break down complex topics, such as transformers, into much more digestible and relatable concepts.

For example, in our final class, we discussed GPUs, memory bottlenecks, and quantisation as a way of reducing the amount of data required. The concept was likened to watching a video in 140p, 360p, or 1080p depending on the amount of data available or the quality of your network. That analogy made the concept much easier to understand and remember.

I also really enjoyed the short quizzes and practical activities. They allowed us to apply the theoretical frameworks directly, which made the learning process more engaging and helped reinforce what we had learned.

The Responsible Innovation sessions were another highlight for me. They brought AI beyond the classroom and connected it to the consequences and decisions we encounter in everyday life. Concepts such as Ubuntu and “Can vs. Should” encouraged me to think more critically about not just what AI can do but what it should do.

Overall, the workshop was an insightful, engaging, and genuinely enjoyable learning experience. I especially appreciated how it combined technical knowledge with practical application and ethical reflection.

Participating in the Train-the-Trainer programme at the University of Lagos under the FATE Foundation Africa Higher Education Institutions AI Upskilling Programme has been a highly valuable experience.

As a health-science educator, researcher, and developer of Neurogauge, an AI-enabled neuroscience and cognitive assessment platform, the training has given me practical knowledge that will strengthen how I apply Artificial Intelligence to medical education, healthcare training, neuroscience research, cognitive assessment, and personalised learning.

The knowledge gained will help me train healthcare educators and students to use AI responsibly, improve learning outcomes, support research innovation, and explore new approaches to early cognitive and neurological assessment through technologies such as Neurogauge.

I am particularly excited about the opportunity to translate the knowledge from this programme into real-world solutions for African healthcare and education systems. I sincerely appreciate AIMS, FATE Foundation, Google DeepMind, the University of Lagos, and all partners for investing in AI capacity development across African higher education.

The workshop was really an impactful and exciting one for me. Each module expanded my knowledge of AI and, more importantly, its applications to solving major African problems by building models that can solve language problems in Africa. Each session strengthened me for an AI-enabled future and equipped me with the knowledge, capacity and confidence to prepare the next generations for a world where AI will shape how we learn, work, innovate and solve problems.

Africa's AI future will not be built by technology alone; it will be built by people and institutions that are prepared to understand it, teach it and apply it. I am fully prepared and ready to prepare my students for an AI-powered future.

The workshop was truly enlightening. The technical classes were well explained, and the “Explain It Back” sessions were awesome, as they helped us explain what we learnt in our own terms. The trainers did an excellent job teaching the topics and explaining areas that were not clear. Even the online classes were very good, and the tasks helped me understand the lectures better. All in all, the Train-the-Trainer workshop was super amazing. Thank you, FATE Foundation; thank you, AIMS.

Participating in the Africa AI Upskilling Programme – AI Research Foundations Nigeria Cohort 1 Workshop was a highly enriching and impactful experience. The programme exposed us to effective AI teaching methodologies, instructional materials, practical resources, and innovative approaches for delivering AI education and research training. The hands-on sessions and collaborative learning environment provided valuable ideas that can be adapted to improve how AI concepts and research skills are taught. Overall, the workshop was a valuable experience that has strengthened my capacity to contribute more effectively to AI education, research, and capacity building in Nigeria.

It was a really amazing experience. Although I already had a bit of a background in AI technology, I was surprised by the number of new perspectives shown in the course, especially the emphasis on responsible innovation and data sovereignty. I learned that it is truly important that the impact of AI is matched with a full-bodied insight into how it can affect people, either positively or negatively.

It was impactful. Capstone made easy.

It was a great experience.

This workshop got me focused with every lecture and activity. Before the workshop, anytime I heard about AI, I thought it was just about prompts. It was interesting to learn that it is more than just that: building your own LLMs, the mathematics and, especially, responsible AI.

The workshop was a very insightful and practical learning experience. I gained a deeper understanding of Large Language Models (LLMs) and the technologies that make them work, particularly neural networks, fine-tuning, GPUs, pre-training, and model development.

What stood out to me most was learning how neural networks learn from data and how fine-tuning can be used to adapt models for specific tasks. I also gained a better appreciation of the important role GPUs play in training and running AI models efficiently.

The hands-on activities made the concepts easier to understand and showed me how AI can be applied to education and other real-world problems, making good use of generative AI. The workshop has increased my confidence and interest in exploring AI and LLM development further, especially in the field of Mathematics Education.

After the workshop, I intend to apply what I have learned to develop innovative AI-powered educational tools that can help students learn, practise, and improve their understanding of Mathematics.

The workshop was quite intensive, but in a very engaging way. We participated actively in the entire learning process through the debates, the presentations, the “Explain It Back” and the “Lecture It Back” sessions. That method actually exposed me to a new way of teaching and, in my opinion, the sessions were just enough to reinforce the concepts that we went through on the Google Skills platform.

Throughout the workshop, I learned valuable knowledge and skills in the area of Artificial Intelligence that have shaped my perspective on how to build, fine-tune and deploy large language models. The knowledge I acquired was in line with my personal projects, such as Sua Pa AI, where I built the first AI that uses shortcode. This experience will help me in my quest to build and deploy my own models.

I learnt how to train my own SLM to tackle pressing issues in Africa, so we can own our data and build AI that fits the African narrative.

Learning the details of fine-tuning was a key achievement for me in this training. Even though I knew about it, my knowledge was shallow.